Notice bibliographique
Résumé
Technology Focus To be quite honest, I am not sure exactly what an "intelligent field" is. Reading through recent literature, I was impressed with the breadth of the intelligent-field topics—topics such as intelligent-well systems, wireless technology, robotics, barriers to implementation, and organizational effects were covered. I struggled to find a common thread that defines the intelligent field. As the deadline for this issue approached, I turned to Webster's Dictionary for help. Webster defines intelligence in terms of the capabilities to reason, plan, solve problems, think abstractly, use language, and learn. Now I was on to something (I thought). Until recently, we would have said these capabilities are all very human, but as technology advances, machines, and now even oil fields, appear to be developing these capabilities. Does this mean as our oil fields are becoming more intelligent, they are becoming more human? Next, I looked for a common thread in the value proposition cited for the intelligent field. The goals of the intelligent field usually are cited as increasing production, reducing capital and operating expenditures, improving total hydrocarbon recovery, and improving safety and environmental performance, but these are the same goals we have had in the upstream oil and gas industry for many years. I returned to the recent literature and extracted what I believe to be the technical capabilities that are required of an intelligent field—continuous monitoring of surface and subsurface operating conditions, assimilation and analysis of huge amounts of complex data from diverse sources, automated recommendation of corrective and proactive actions, and optimization of the total asset over its life cycle. However, these technical capabilities cannot be exploited without the advancement and evolution of work processes and the capabilities of our people. As you read through the literature, remember that the promises of the intelligent field cannot be delivered through technology alone. As technologies advance, we must commit to the advancement of our processes and to the development of our people. Only through a holistic approach to technology, process, and people can we realize the goals of the intelligent field. Intelligent Fields Technology additional reading available at the SPE eLibrary: www.spe.org SPE 110296 • "Production Optimization by Real-Time Modeling and Alarming: The Sendji Field Case" by Jacques Danquigny, SPE, Total, et al. SPE 110525 • "Optimizing the Production System Using Real-Time Measurements: A Piece of the Digital-Oilfield Puzzle" by Robert B. Thompson, Aethon, et al. SPE 112152 • "A Standard Solution for Upstream Oil and Gas Surveillance" by Mark L. Crawford, SPE, ExxonMobil, et al.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».